






"""simple docstring"""


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE   =   '▁'

__SCREAMING_SNAKE_CASE   =   {'vocab_file': 'sentencepiece.bpe.model'}

__SCREAMING_SNAKE_CASE   =   {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

__SCREAMING_SNAKE_CASE   =   {
    'xlm-roberta-base': 5_12,
    'xlm-roberta-large': 5_12,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_12,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_12,
    'xlm-roberta-large-finetuned-conll03-english': 5_12,
    'xlm-roberta-large-finetuned-conll03-german': 5_12,
}




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Dict      =  VOCAB_FILES_NAMES
					lowercase_    :				int      =  PRETRAINED_VOCAB_FILES_MAP
					lowercase_    :				List[str]      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase_    :				Dict      =  ["""input_ids""", """attention_mask"""]
					def __init__(							self							:	Optional[Any]     , UpperCAmelCase							:	List[str]     , UpperCAmelCase							:	List[str]="<s>"     , UpperCAmelCase							:	Any="</s>"     , UpperCAmelCase							:	Tuple="</s>"     , UpperCAmelCase							:	Optional[Any]="<s>"     , UpperCAmelCase							:	Optional[Any]="<unk>"     , UpperCAmelCase							:	List[Any]="<pad>"     , UpperCAmelCase							:	Tuple="<mask>"     , UpperCAmelCase							:	Optional[Dict[str, Any]] = None     , **UpperCAmelCase							:	Dict     , )	->       None:
											# Mask token behave like a normal word, i.e. include the space before it
											lowerCAmelCase   :Tuple						=			AddedToken(UpperCAmelCase     , lstrip=UpperCAmelCase     , rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase     , UpperCAmelCase   ) else mask_token

											lowerCAmelCase   :Tuple						=			{} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=UpperCAmelCase     , eos_token=UpperCAmelCase     , unk_token=UpperCAmelCase     , sep_token=UpperCAmelCase     , cls_token=UpperCAmelCase     , pad_token=UpperCAmelCase     , mask_token=UpperCAmelCase     , sp_model_kwargs=self.sp_model_kwargs     , **UpperCAmelCase     , )

											lowerCAmelCase   :List[str]						=			spm.SentencePieceProcessor(**self.sp_model_kwargs   )
											self.sp_model.Load(str(UpperCAmelCase   )   )
											lowerCAmelCase   :Union[str, Any]						=			vocab_file

											# Original fairseq vocab and spm vocab must be "aligned":
											# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
											# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
											# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
											# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

											# Mimic fairseq token-to-id alignment for the first 4 token
											lowerCAmelCase   :Optional[Any]						=			{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

											# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
											lowerCAmelCase   :Union[str, Any]						=			1

											lowerCAmelCase   :Any						=			len(self.sp_model   ) + self.fairseq_offset
											lowerCAmelCase   :List[Any]						=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}
					def __getstate__(							self							:	Dict   )	->       List[str]:
											lowerCAmelCase   :Any						=			self.__dict__.copy()
											lowerCAmelCase   :Tuple						=			None
											lowerCAmelCase   :List[str]						=			self.sp_model.serialized_model_proto()
											return state
					def __setstate__(							self							:	str     , UpperCAmelCase							:	List[str]   )	->       Dict:
											lowerCAmelCase   :Union[str, Any]						=			d

											# for backward compatibility
											if not hasattr(self     , 'sp_model_kwargs'   ):
																	lowerCAmelCase   :Dict						=			{}

											lowerCAmelCase   :Union[str, Any]						=			spm.SentencePieceProcessor(**self.sp_model_kwargs   )
											self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )
					def        UpperCAmelCase__ (							self							:	Optional[int]     , UpperCAmelCase							:	List[int]     , UpperCAmelCase							:	Optional[List[int]] = None   )	->       List[int]:

											if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											lowerCAmelCase   :List[str]						=			[self.cls_token_id]
											lowerCAmelCase   :Optional[int]						=			[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep
					def        UpperCAmelCase__ (							self							:	str     , UpperCAmelCase							:	List[int]     , UpperCAmelCase							:	Optional[List[int]] = None     , UpperCAmelCase							:	bool = False   )	->       List[int]:

											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=UpperCAmelCase     , token_ids_a=UpperCAmelCase     , already_has_special_tokens=UpperCAmelCase   )

											if token_ids_a is None:
																	return [1] + ([0] * len(UpperCAmelCase   )) + [1]
											return [1] + ([0] * len(UpperCAmelCase   )) + [1, 1] + ([0] * len(UpperCAmelCase   )) + [1]
					def        UpperCAmelCase__ (							self							:	Optional[int]     , UpperCAmelCase							:	List[int]     , UpperCAmelCase							:	Optional[List[int]] = None   )	->       List[int]:
											lowerCAmelCase   :Optional[int]						=			[self.sep_token_id]
											lowerCAmelCase   :Optional[Any]						=			[self.cls_token_id]

											if token_ids_a is None:
																	return len(cls + token_ids_a + sep   ) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]
					@property
					def        UpperCAmelCase__ (							self							:	int   )	->       Optional[Any]:
											return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token
					def        UpperCAmelCase__ (							self							:	Any   )	->       Dict:
											lowerCAmelCase   :List[Any]						=			{self.convert_ids_to_tokens(UpperCAmelCase   ): i for i in range(self.vocab_size   )}
											vocab.update(self.added_tokens_encoder   )
											return vocab
					def        UpperCAmelCase__ (							self							:	Any     , UpperCAmelCase							:	str   )	->       List[str]:
											return self.sp_model.encode(UpperCAmelCase     , out_type=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Optional[int]     , UpperCAmelCase							:	List[str]   )	->       Tuple:
											if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
											lowerCAmelCase   :List[Any]						=			self.sp_model.PieceToId(UpperCAmelCase   )

											# Need to return unknown token if the SP model returned 0
											return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
					def        UpperCAmelCase__ (							self							:	str     , UpperCAmelCase							:	int   )	->       str:
											if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(index - self.fairseq_offset   )
					def        UpperCAmelCase__ (							self							:	Tuple     , UpperCAmelCase							:	Any   )	->       Union[str, Any]:
											lowerCAmelCase   :Dict						=			''.join(UpperCAmelCase   ).replace(UpperCAmelCase     , ' '   ).strip()
											return out_string
					def        UpperCAmelCase__ (							self							:	Optional[int]     , UpperCAmelCase							:	str     , UpperCAmelCase							:	Optional[str] = None   )	->       Tuple[str]:
											if not os.path.isdir(UpperCAmelCase   ):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
																	return
											lowerCAmelCase   :Any						=			os.path.join(
											    UpperCAmelCase     , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

											if os.path.abspath(self.vocab_file   ) != os.path.abspath(UpperCAmelCase   ) and os.path.isfile(self.vocab_file   ):
																	copyfile(self.vocab_file     , UpperCAmelCase   )
											elif not os.path.isfile(self.vocab_file   ):
																	with open(UpperCAmelCase     , 'wb'   ) as fi:
																							lowerCAmelCase   :str						=			self.sp_model.serialized_model_proto()
																							fi.write(UpperCAmelCase   )

											return (out_vocab_file,)







"""simple docstring"""


import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
		import onnxruntime as ort




@nightly
@require_onnxruntime
@require_torch_gpu
class 						__UpperCamelCase     (     unittest.TestCase						):
					@property
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Dict:
											return (
											    "CUDAExecutionProvider",
											    {
											        "gpu_mem_limit": "15000000000",  # 15GB
											        "arena_extend_strategy": "kSameAsRequested",
											    },
											)
					@property
					def        UpperCAmelCase__ (							self							:	int   )	->       Optional[Any]:
											lowerCAmelCase   :Any						=			ort.SessionOptions()
											lowerCAmelCase   :Dict						=			False
											return options
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       str:
											lowerCAmelCase   :Union[str, Any]						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo.png'   )
											lowerCAmelCase   :str						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'   )
											lowerCAmelCase   :List[Any]						=			load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'   )

											# using the PNDM scheduler by default
											lowerCAmelCase   :Tuple						=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
											    'CompVis/stable-diffusion-v1-4'     , revision='onnx'     , safety_checker=UpperCAmelCase     , feature_extractor=UpperCAmelCase     , provider=self.gpu_provider     , sess_options=self.gpu_options     , )
											pipe.set_progress_bar_config(disable=UpperCAmelCase   )

											lowerCAmelCase   :Optional[Any]						=			'A red cat sitting on a park bench'

											lowerCAmelCase   :Optional[Any]						=			np.random.RandomState(0   )
											lowerCAmelCase   :Union[str, Any]						=			pipe(
											    prompt=UpperCAmelCase     , image=UpperCAmelCase     , mask_image=UpperCAmelCase     , strength=0.7_5     , guidance_scale=7.5     , num_inference_steps=15     , generator=UpperCAmelCase     , output_type='np'     , )

											lowerCAmelCase   :Optional[int]						=			output.images[0]

											assert image.shape == (512, 512, 3)
											assert np.abs(expected_image - image   ).max() < 1e-2